A probabilistic-based analysis for wind distribution determination of a runway

2021 ◽  
Vol 93 (2) ◽  
pp. 284-297
Author(s):  
Ahmet Esat Suzer ◽  
Aziz Kaba
Keyword(s):  
Wind Speed ◽  
Density Function ◽  
Good Choice ◽  
Pattern Search ◽  
Likelihood Method ◽  
Wind Regime ◽  
Wind Speed Profile ◽  
Content Type ◽  
Wind Distribution ◽  
Two Parameter

Purpose The purpose of this study is to describe precisely the wind speed regime and characteristics of a runway of an International Airport, the north-western part of Turkey. Design methodology approach Three different probability distributions, namely, Inverse Gaussian (IG), widely used two-parameter Weibull and Rayleigh distributions in the literature, are used to represent wind regime and characteristics of the runway. The parameters of each distribution are estimated by the pattern search (PS)-based heuristic algorithm. The results are compared with the other three methods-based numerical computation, including maximum-likelihood method, moment method (MoM) and power density method, respectively. To evaluate the fitting performance of the proposed method, several statistical goodness tests including the mostly used root mean square error (RMSE) and chi-squared (X2) are conducted. Findings In the light of the statistical goodness tests, the results of the IG-based PS attain better performance than the classical Weibull and Rayleigh functions. Both the RMSE and X2 values achieved by the IG-based PS method lower than that of Weibull and Rayleigh distributions. It exhibits a better fitting performance with 0.0074 for RMSE and 0.58 × 10−4 for X2 for probability density function (PDF) in 2012 and with RMSE of 0.0084 and X2 of 0.74 × 10−4 for PDF in 2013. As regard the cumulative density function of the measured wind data, the best results are found to be Weibull-based PS with RMSE of 0.0175 and X2 of 3.25 × 10−4 in 2012. However, Weibull-based MoM shows more excellent ability in 2013, with RMSE of 0.0166 and X2 of 2.94 × 10−4. Consequently, it is considered that the results of this study confirm that IG-based PS with the lowest error value can a good choice to model more accurately and characterize the wind speed profile of the airport. Practical implications This paper presents a realistic point of view regarding the wind regime and characteristics of an airport. This study may cast the light on researchers, policymakers, policy analysts and airport designers intending to investigate the wind profile of a runway at the airport in the world and also provide a significant pathway on how to determine the wind distribution of the runway. Originality value Instead of the well-known Weibull distribution for the representing of wind distribution in the literature, in this paper, IG distribution is used. Furthermore, the suitability of IG to represent the wind distribution is validated when compared with two-parameter Weibull and Rayleigh distributions. Besides, the performance and efficiency of PS have been evaluated by comparing it with other methods.

scholarly journals Analysis of wind speed characteristics using different distribution models in Medan City, Indonesia

2021 ◽  
Vol 12 (2) ◽  
pp. 1102
Author(s):  
Suwarno Suwarno ◽  
Ismail Yusuf ◽  
M. Irwanto ◽  
Ayong Hiendro
Keyword(s):  
Wind Speed ◽  
Density Function ◽  
Weibull Model ◽  
Exponential Model ◽  
Distribution Functions ◽  
Cumulative Probability ◽  
Distribution Models ◽  
Shape Factors ◽  
Wind Characteristics ◽  
Wind Distribution

<span lang="EN-CA">Estimating wind speed characteristics plays an essential role in designing a wind power plant at a selected location. In this study, the Weibull, gamma, and exponential distribution models were proposed to estimate and analyze the wind speed parameters and distribution functions. Real measured data were collected from Medan City, Indonesia. The scale and shape factors of wind distribution for three years data were calculated. The observed cumulative probability of the three models was compared to predicted wind characteristics. The probability density function (PDF) and the cumulative density function (CDF) of wind speed were also analyzed. The results showed that the Weibull model was the best model to determine PDF, while the exponential model was the best model to determine CDF for the Medan City wind site.</span>

A python program to model and analyze wind speed data

2021 ◽  
Vol 9 (6) ◽  
pp. 769-784
Keyword(s):  
Maximum Likelihood ◽  
Wind Speed ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Weibull Function ◽  
Method Of Moment ◽  
Wind Speed Data ◽  
Statistical Errors ◽  
Wind Potential ◽  
Wind Distribution

A python program has been developed to analyze wind distributions using the Weibull density function. A two-parameter Weibull function is frequently used to model and assess wind potential and wind distribution. This python program finds first Weibull parameters from the recorded wind data by five different methods, namely, Empirical Method(EPM), Method of Moment (MoM), Energy Pattern Factor Method (EPFM), Maximum Likelihood Method (MLM), Modified Maximum Likelihood Method (MMLM), the parameters are then used to find theoretically fitted pdfs. The program is implemented on wind distribution of two cities of Pakistan (Chakri and Sadiq Abad). The program-generated pdfs were plotted with the histogram of recorded data, the fitting was excellent. To check the validity of the fitted pdfs, statistical errors Root Mean Square (RMSE), MeanAbsolute Percent Error (MAPE), Mean Absolute Error (MABE), and Chi-square statistic are calculated. In all cases,these statistical errors are well below the acceptance range. Both pictorial results and numerical values of statistical errors indicate the performance of the python program to analyze wind speed data

Prediction of wind speed profile using two artificial neural network models: an ab initio investigation in the Bapouh’s city, Cameroon

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Franck Armel Talla Konchou ◽  
Pascalin Tiam Kapen ◽  
Steve Brice Kenfack Magnissob ◽  
Mohamadou Youssoufa ◽  
René Tchinda
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Speed Profile ◽  
Multi Layer Perceptron ◽  
The West ◽  
Wind Speed Profile ◽  
Content Type ◽  
West Region ◽  
Artificial Neural ◽  
Nonlinear Autoregressive

Purpose This paper aims to investigate the profile of the wind speed of a Cameroonian city for the very first time, as there is a growing trend for new wind energy installations in the West region of Cameroon. Two well-known artificial neural networks, namely, multi-layer perceptron (MLP) and nonlinear autoregressive network with exogenous inputs (NARX), were used to model the wind speed profile of the city of Bapouh in the West-region of Cameroon. Design/methodology/approach In this work, the profile of the wind speed of a Cameroonian city was investigated for the very first time since there is a growing trend for new wind energy installations in the West region of Cameroon. Two well-known artificial neural networks namely multi-layer perceptron (MLP) and nonlinear autoregressive network with exogenous inputs (NARX) were used to model the wind speed profile of the city of Bapouh in the West-region of Cameroon. The meteorological data were collected every 10 min, at a height of 50 m from the NASA website over a period of two months from December 1, 2016 to January 31, 2017. The performance of the model was evaluated using some well-known statistical tools, such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The input variables of the model were the mean wind speed, wind direction, maximum pressure, maximum temperature, time and relative humidity. The maximum wind speed was used as the output of the network. For optimal prediction, the influence of meteorological variables was investigated. The hyperbolic tangent sigmoid (Tansig) and linear (Purelin) were used as activation functions, and it was shown that the combination of wind direction, maximum pressure, maximum relative humidity and time as input variables is the best combination. Findings Maximum pressure, maximum relative humidity and time as input variables is the best combination. The correlation between MLP and NARX was computed. It was found that the MLP has the highest correlation when compared to NARX. Originality/value Two well-known artificial neural networks namely multi-layer perceptron (MLP) and nonlinear autoregressive network with exogenous inputs (NARX) were used to model the wind speed profile.

Estimation of monthly wind speed distribution basing on hybrid Weibull distribution

2016 ◽  
Vol 13 (6) ◽  
pp. 509-515 ◽  
Author(s):  
Razika Ihaddadene ◽  
Nabila Ihaddadene ◽  
Marouane Mostefaoui
Keyword(s):  
Wind Speed ◽  
Numerical Methods ◽  
Moment Method ◽  
Likelihood Method ◽  
Statistical Parameters ◽  
Chi Square ◽  
Content Type ◽  
Speed Distribution ◽  
Wind Speed Distribution ◽  
The Moment

Purpose The purpose of this paper is to analyze and compare four numerical methods to estimate the most suitable one which describes wind speed distribution of M’Sila, a province of northern Algeria. Design/methodology/approach The site chosen in this investigation is characterized by calm winds; in this case, the appropriate wind speed distribution is that of hybrid Weibull. Findings The four numerical methods used in the present paper are the maximum likelihood method, the graphical method, the moment method and the energy pattern factor method. The hybrid Weibull distributions using the abovementioned approaches are compared with the measured data via three statistical parameters, namely, the correlation coefficient, the root mean square error and the Chi-square error. Originality/value The obtained results showed that the moment method is the suitable one in describing month and annual wind speed hybrid Weibull parameters of this region.

A new lifetime distribution with applications in inventory and insurance

2018 ◽  
Vol 35 (2) ◽  
pp. 527-544 ◽  
Author(s):  
Shovan Chowdhury ◽  
Asok K. Nanda
Keyword(s):  
Risk Management ◽  
Probability Distribution ◽  
Density Function ◽  
Inventory Management ◽  
Geometric Distribution ◽  
Likelihood Method ◽  
Attractive Alternative ◽  
Content Type ◽  
Bounded Support ◽  
Better Than

Purpose The purpose of this paper is to introduce a new probability density function having both unbounded and bounded support with a wider applicability. While the distribution with bounded support on [0, 1] has applications in insurance and inventory management with ability to fit risk management data on proportions better than existing bounded distributions, the same with unbounded support is used as a lifetime model and is considered as an attractive alternative to some existing models in the reliability literature. Design/methodology/approach The new density function, called modified exponential-geometric distribution is derived from the exponential-geometric distribution introduced by Adamidis and Loukas (1998). The support of the density function is shown to be both unbounded and bounded depending on the values of one of the shape parameters. Various properties of the density function are studied in detail and the parameters are estimated through maximum likelihood method of estimation. A number of applications related to reliability, insurance and inventory management are exhibited along with some useful data analysis. Findings A single probability distribution with both unbounded and bounded support, which does not seem to exist in the reliability literature, is introduced in this paper. The proposed density function exhibits varying shapes including U-shape, and the failure rate also shows increasing, decreasing and bathtub shapes. The Monte Carlo simulation shows that the estimates of the parameters are quite stable with low standard errors. The distribution with unbounded support is shown to have competitive features for lifetime modeling through analysis of two data sets. The distribution with bounded support on [0, 1] is shown to have application in insurance and inventory management and is found to t data on proportions related to risk management better than some existing bounded distributions. Originality/value The authors introduce an innovative probability distribution which contributes significantly in insurance and inventory management besides its remarkable statistical and reliability properties.

scholarly journals An Assessment of Onshore and Offshore Wind Energy Potential in India Using Moth Flame Optimization

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3063 ◽  
Author(s):  
Krishnamoorthy R ◽  
Udhayakumar K ◽  
Kannadasan Raju ◽  
Rajvikram Madurai Elavarasan ◽  
Lucian Mihet-Popa
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Likelihood Method ◽  
Wind Speeds ◽  
Speed Distribution ◽  
Wind Speed Distribution ◽  
South West Monsoon ◽  
Wind Distribution ◽  
Wind Resource

Wind energy is one of the supremely renewable energy sources and has been widely established worldwide. Due to strong seasonal variations in the wind resource, accurate predictions of wind resource assessment and appropriate wind speed distribution models (for any location) are the significant facets for planning and commissioning wind farms. In this work, the wind characteristics and wind potential assessment of onshore, offshore, and nearshore locations of India—particularly Kayathar in Tamilnadu, the Gulf of Khambhat, and Jafrabad in Gujarat—are statistically analyzed with wind distribution methods. Further, the resource assessments are carried out using Weibull, Rayleigh, gamma, Nakagami, generalized extreme value (GEV), lognormal, inverse Gaussian, Rician, Birnbaum–Sandras, and Bimodal–Weibull distribution methods. Additionally, the advent of artificial intelligence and soft computing techniques with the moth flame optimization (MFO) method leads to superior results in solving complex problems and parameter estimations. The data analytics are carried out in the MATLAB platform, with in-house coding developed for MFO parameters estimated through optimization and other wind distribution parameters using the maximum likelihood method. The observed outcomes show that the MFO method performed well on parameter estimation. Correspondingly, wind power generation was shown to peak at the South West Monsoon periods from June to September, with mean wind speeds ranging from 9 to 12 m/s. Furthermore, the wind speed distribution method of mixed Weibull, Nakagami, and Rician methods performed well in calculating potential assessments for the targeted locations. Likewise, the Gulf of Khambhat (offshore) area has steady wind speeds ranging from 7 to 10 m/s with less turbulence intensity and the highest wind power density of 431 watts/m2. The proposed optimization method proves its potential for accurate assessment of Indian wind conditions in selected locations.

scholarly journals Two Improved Mixture Weibull Models for the Analysis of Wind Speed Data

2012 ◽  
Vol 51 (7) ◽  
pp. 1321-1332 ◽  
Author(s):  
Xu Qin ◽  
Jiang-she Zhang ◽  
Xiao-dong Yan
Keyword(s):  
Wind Speed ◽  
Weibull Distribution ◽  
Likelihood Method ◽  
Distribution Models ◽  
Component Mixture ◽  
Wind Speed Data ◽  
Two Component ◽  
Weibull Models ◽  
Improved Model ◽  
Two Parameter

AbstractIn this paper, the authors propose two improved mixture Weibull distribution models by adding one or two location parameters to the existing two-component mixture two-parameter Weibull distribution [MWbl(2, 2)] model. One improved model is the mixture two-parameter Weibull and three-parameter Weibull distribution [MWbl(2, 3)] model. The other improved model is the two-component mixture three-parameter Weibull distribution [MWbl(3, 3)] model. In contrast to existing literature, which has focused on the MWbl(2, 2) and the typical Weibull distribution models, the authors apply the MWbl(2, 3) model and MWbl(3, 3) model to fit the distribution of wind speed data with nearly zero percentages of null wind speed. The parameters of the two improved models are estimated by the maximum likelihood method in which the maximization problem is regarded as a nonlinear programming problem with only inequality constraints and is solved numerically by the interior-point method. The experimental results show that the mixture Weibull models proposed in this paper are more flexible than the existing models for the analysis of wind speed data in practice.

scholarly journals Wind Speed Profile Statistics from Acoustic Soundings at a Black Sea Coastal Site

Atmosphere ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1122
Author(s):  
Damyan Barantiev ◽  
Ekaterina Batchvarova
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Remote Sensing Data ◽  
Quadratic Functions ◽  
Likelihood Method ◽  
Internal Boundary ◽  
Wind Speed Profile ◽  
Data Set ◽  
Coastal Meteorology ◽  
Diurnal Wind

More than seven years of remote sensing data with high spatial and temporal resolution were investigated in this study. The 20-min moving averaged wind profiles form the acoustic sounding with Scintec MFAS sodar were derived every 10 min. The profiles covered from 30 to 600 m height with vertical resolution of 10 m. The wind speed probability and the Weibull distribution parameters were calculated by the maximum likelihood method at each level and then the profiles of the Weibull scale and shape parameters were analyzed. Diurnal wind speed at heights above 200 m has shown a well-expressed increase in the averaged values during the night hours, while during the day lower wind speeds were observed. The reversal height was explored from spatially and temporally homogenized diurnal wind speed data with applied quadratic functions for better interpretation of the results. In addition, analyses by type of air masses (land or sea air mass) were performed. One of the outcomes of the study was assessment of the internal boundary layer height, which was estimated to 50–80 m at the location of the sodar. The obtained information forms the basis for climatological insights on the vertical structure of the coastal boundary layer and is unique long-term data set important not only for Bulgaria but for coastal meteorology in general.

scholarly journals The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters

2018 ◽  
Vol 7 (2) ◽  
pp. 139-150 ◽  
Author(s):  
Adekunlé Akim Salami ◽  
Ayité Sénah Akoda Ajavon ◽  
Mawugno Koffi Kodjo ◽  
Seydou Ouedraogo ◽  
Koffi-Sa Bédja
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Time Series Data ◽  
Empirical Method ◽  
Likelihood Method ◽  
Series Data ◽  
New Approach ◽  
Weibull Parameters ◽  
Distribution Histogram ◽  
Mean Wind Speed

In this article, we introduced a new approach based on graphical method (GPM), maximum likelihood method (MLM), energy pattern factor method (EPFM), empirical method of Justus (EMJ), empirical method of Lysen (EML) and moment method (MOM) using the even or odd classes of wind speed series distribution histogram with 1 m/s as bin size to estimate the Weibull parameters. This new approach is compared on the basis of the resulting mean wind speed and its standard deviation using seven reliable statistical indicators (RPE, RMSE, MAPE, MABE, R2, RRMSE and IA). The results indicate that this new approach is adequate to estimate Weibull parameters and can outperform GPM, MLM, EPF, EMJ, EML and MOM which uses all wind speed time series data collected for one period. The study has also found a linear relationship between the Weibull parameters K and C estimated by MLM, EPFM, EMJ, EML and MOM using odd or even class wind speed time series and those obtained by applying these methods to all class (both even and odd bins) wind speed time series. Another interesting feature of this approach is the data size reduction which eventually leads to a reduced processing time.Article History: Received February 16th 2018; Received in revised form May 5th 2018; Accepted May 27th 2018; Available onlineHow to Cite This Article: Salami, A.A., Ajavon, A.S.A., Kodjo, M.K. , Ouedraogo, S. and Bédja, K. (2018) The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters. Int. Journal of Renewable Energy Development 7(2), 139-150.https://doi.org/10.14710/ijred.7.2.139-150

scholarly journals Atmospheric influences on infrared-laser signals used for occultation measurements between Low Earth Orbit satellites

2011 ◽  
Vol 4 (10) ◽  
pp. 2273-2292 ◽  
Author(s):  
S. Schweitzer ◽  
G. Kirchengast ◽  
V. Proschek
Keyword(s):  
Wind Speed ◽  
Solar Radiation ◽  
Thermal Radiation ◽  
Rayleigh Scattering ◽  
Infrared Laser ◽  
Low Earth Orbit ◽  
Earth Orbit ◽  
Wind Speed Profile ◽  
Upper Troposphere ◽  
Trace Species

Abstract. LEO-LEO infrared-laser occultation (LIO) is a new occultation technique between Low Earth Orbit (LEO) satellites, which applies signals in the short wave infrared spectral range (SWIR) within 2 μm to 2.5 μm. It is part of the LEO-LEO microwave and infrared-laser occultation (LMIO) method that enables to retrieve thermodynamic profiles (pressure, temperature, humidity) and altitude levels from microwave signals and profiles of greenhouse gases and further variables such as line-of-sight wind speed from simultaneously measured LIO signals. Due to the novelty of the LMIO method, detailed knowledge of atmospheric influences on LIO signals and of their suitability for accurate trace species retrieval did not yet exist. Here we discuss these influences, assessing effects from refraction, trace species absorption, aerosol extinction and Rayleigh scattering in detail, and addressing clouds, turbulence, wind, scattered solar radiation and terrestrial thermal radiation as well. We show that the influence of refractive defocusing, foreign species absorption, aerosols and turbulence is observable, but can be rendered small to negligible by use of the differential transmission principle with a close frequency spacing of LIO absorption and reference signals within 0.5%. The influences of Rayleigh scattering and terrestrial thermal radiation are found negligible. Cloud-scattered solar radiation can be observable under bright-day conditions, but this influence can be made negligible by a close time spacing (within 5 ms) of interleaved laser-pulse and background signals. Cloud extinction loss generally blocks SWIR signals, except very thin or sub-visible cirrus clouds, which can be addressed by retrieving a cloud layering profile and exploiting it in the trace species retrieval. Wind can have a small influence on the trace species absorption, which can be made negligible by using a simultaneously retrieved or a moderately accurate background wind speed profile. We conclude that the set of SWIR channels proposed for implementing the LMIO method (Kirchengast and Schweitzer, 2011) provides adequate sensitivity to accurately retrieve eight trace species of key importance to climate and atmospheric chemistry (H2O, CO2, 13CO2, C18OO, CH4, N2O, O3, CO) in the upper troposphere/lower stratosphere region outside clouds under all atmospheric conditions. Two further species (HDO, H218O) can be retrieved in the upper troposphere.

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